Alpha From Alternative Data

29th September 2017

Presenter: Emmett Kilduff (Founder & CEO)

Mobile: +353 (0)86 7772198

Email: emmett.kilduff@eaglealpha.com

Table Of Contents

1

1: Adoption2: The Global Landscape

3: The Asian Landscape

4: Case Studies 5: Eagle Alpha

1

1: Adoption

A1, 6%

A2, 70%

A3, 23%

Alternative Data Will Fundamentally Change The Investment Landscape

2

Q: At JPMorgan’s annual quant conference last May, 237 asset managers were asked: what is your opinion of Big Data / Machine Learning?

A1: 6% said “fad – investor interest will decline”.

A2: 70% said “evolution – it’s importance will gradually grow for all investors”.

A3: 23% said “revolution – will lead to rapid changes to investment landscape”.

Alternative Data Is Not New. >50 Innovative Firms Have Been Working With Alternative Data For Years e.g. WorldQuant

3

GSAM Is An Innovator

4

Blackrock Is An Innovator

5

Schroders Is An Innovator

6

Balyasny & Citadel Are Innovators

7

Blue Mountain & DeShaw Are Innovators

8

Alternative Data Adoption Will ‘Cross The Chasm’ By The End Of 2018

Quantitative fundsCutting-

edge

firms

Large

number of

quant

funds

Bulk of firms Small minority

Hedge funds Select few

Quant-a-

mental

firms

Bulk of firms Minority

Mutual funds <5 firms

Quant-a-

mental

firms

The more dynamic

mutual funds

Bulk of firms

(conservatives)

The rest

(skeptics)

Source: Geoffrey Moore’s book ‘Crossing the Chasm’, Eagle Alpha

50+ firms have been working

with alternative data for years.

Among discretionary managers

today, 24% are using ‘big data’

today. Source: Barclays.

Today 20% of HFs >$1bn AUM

have headcount or 50% of a

persons time. Source: Jefferies

Eagle Alpha currently has a

dialogue with 226 firms. This is

increasing rapidly.

70% of firms say importance of

big data will gradually grow for

all firms. Source: JPMorgan.

80% of firms want greater

access to alternative data.

Source: Greenwich survey.

9

A Key Driver Of Adoption Is Firms Adopting A ‘Quantamental’ Approach

Consumer

Insights

Source: Morgan Stanley 10

A Key Driver Of Adoption Is To Increase Profit Margins

Consumer

Insights

Source: Quinlan Associates 11

Benefits Of Using Alternative Data

Consumer

Insights

Source: Quinlan Associates 12

90% Of Alt Data Users Have Seen The Return They Hoped For, 95% Said It Helps Explain Their Strategy To Clients

13

Use Cases

14

2: The Global Landscape

Eagle Alpha’s Database Currently Has 528 Datasets Split Into 24 Categories Of Alternative Data

Pricing

(78)

Employment

(11)

Web

Crawled

(20)

Mobile

App

Usage

(33)

Reviews

&

Ratings

(41)

Social

Media

(62)

Sentiment

(26)

Online

Search

(19)

Expert

Views

(15)

Store

Locations

(15)

Advertising

(27)

Event

Detection

(27)

Trade

(10)

Data

Aggregators

(45)

Consumer

Credit

(15)

Open

Data

(53)

Public

Sector

(54)

B2B(13)

Geo-

Location

(36)

Satellite

&

Weather

(35)

Internet

of

Things

(4)

Consumer

Transaction

(31)

Business

Insights

(83)

Consumer

Insights

ESG

(15)

Note: the number in each octagon represents the number of alternative datasets that Eagle Alpha has identified in that category.

15

Where Does Consumer Transaction Data Come From?

Online Transactions

Point of SaleTransactions

16

Credit Card Company

Personal financeapps, loyalty programs

Customer Bank Merchant Bank

POS terminal& technologies

Merchant provides purchase data to market research firms

Merchant financial programs & services

Payment processorMerchant emails receipt to customer

Credit card issuing bank

Many Parties Involved In A Consumer Transaction

17

Consumer Transaction Data Sources (U.S.)

Apps

Eagle Alpha Data Partner(China)

Eagle Alpha Data Partner

Eagle Alpha Data Partner

18

Example Of A Dataset: Employment Dataset

19

Vendor Overview

• The company aggregates labour market data from company websites in the U.S. and worldwide.

• The data is updated daily and contains no duplicate listings or job pollution.

• Eagle Alpha Labour Market Data partner has become a leading provider of labour market data and analytics.

• The index includes every industry and every job type and doesn’t include jobs from job boards or job sites.

Data Set

Description

• Disclaimer by vendor regarding data set: Information provided

through this document is not advice and is subject to change.

We may amend, update, suspend or delete any information in

the content without notice at any time and at our sole

discretion. The provider has represented to Eagle Alpha that

the following information is true at the time of writing [June

2017]

Data Set Overview

Geography

USA and worldwide

Coverage

4 million job

openings sourced

from 30,000

company websites

Mapped to

Tickers

Yes

History

Since

2007

Collection

Frequency

Real-Time

Delivery

Frequency

Real-Time

Lag TimeDelivery

Method

Platform,

AWS, FTTP

Legal & Compliance

• Granular: A proprietary dataset of high quality and real-time updates with dozen primary and some secondary fields.

• Breadth: Index of 4 million job openings (~70% of all job openings in U.S.) and has jobs sourced from 30,000 companies..

• Representative: Index is updated daily for real-time jobs and data feeds and includes jobs and companies from 185 countries?

Legal• The provider is authorized to sell the data set.

• The data sets do not include any PII.

Compliance• The data set does not contain any MNPI.

• The data set is available to any buy side firm

i.e. no exclusivity offered to interested parties.

Related Tickers

Alpha: Use Cases

Trial & SubscriptionE.A Standard Trial Agreement

No

Trial Data

Full Access to Historical

Data

Trial Duration

Up to 90 days

• Examine the crawled data covering approximately 15k companies, of which around 3.5k companies are publicly traded in the US.

• Two categories of variables: Jobs Created and Jobs Active

• Form monthly and annual portfolios by dividing the sample of firms based on the 10 variables into both deciles and quintiles.

• The top portfolio is the portfolio of 10% of the firms and bottom portfolio is 10% of the firms where the variable is the lowest.

• Calculate the hedge return which is the top portfolio average return minus the bottom portfolio average return

No

• Corresponds to 3500 tickers: examples include: AMZ:US, PYPL:US, GRUB:US, UBER:US, AAPL:US, NMG:US, SQ:US, LUV:US

Pricing

Contact Eagle Alpha at enquiries@eaglealpha.com

Alpha: Example

• Eagle Alpha’s predictive model, demonstrates alpha opportunity in the variables where Jobs Active produce the highest and most consistent returns.

• Jobs Active: Calculated as is or normalized - Yearly hedge returns are between 6-8%

• Growth-returns are U-shaped where the top and bottom portfolios are higher than the middle portfolios

• Jobs Created: Calculated as is or normalized - Yearly hedge returns are between 2-4% (portfolio returns are not linear)

Eagle Alpha Recently Published An 82 Page Report That Outlines The Applications Of Alternative Data

20

The Report Includes 20 Case Studies That Are Categorised By Asset Class And Type Of Manager

21

3: The Asian Landscape

The Alt Data Landscape In China Based On Our Current Database – We Have Just Hired A Person To Focus On China

22

Advertising 6

App Usage & Web Traffic 9

Business Insights 2

Consumer Credit 6

Consumer Transactions 6

Data Aggregators 15

Employment 1

ESG 1

Event Detection 1

Geo-Location 6

Open Data 6

Pricing 3

Public Sector 5

Reviews & Rating 1

Satellite & Weather 1

Sentiment 6

Store Location 1

Web Crawled Data 2

Several Datasets Provide Granularity That Traditional Datasets Do Not Offer e.g. China Autos Dataset

23

Vendor Overview

• The provider is the leading provider of “data-supported business decisions” for the automotive industry in China.

• With the largest market share in the autos segment in China, their key data products take 60% market share, which reaches 80% market share

in JV automakers.

• They have assembled a unique panel consisting of over 1,300 contributing co-operating Chinese dealerships.

• Eagle Alpha have an exclusive partnership with this provider to distribute these powerful data sets to the finance vertical.

Data Set

Description

• Disclaimer by vendor regarding data set: Information provided

through this document is not advice and is subject to change.

We may amend, update, suspend or delete any information in

the content without notice at any time and at our sole

discretion. The provider has represented to Eagle Alpha that

the following information is true at the time of writing [17 Jan

2017]

Data Set Overview

Geography

China

Coverage

National, with

regional breakdowns

Mapped to

Tickers

No

History

Since

2012

Collection

Frequency

Mixed – Month/ Bi-

Monthly

Delivery

Frequency

Mixed –

Month/ Bi-

Monthly

Lag Time

Between 5

& 20 days

Delivery

Method

API, CSV

Legal & Compliance

• Transaction Price: Average transaction price of automobiles at a model, sub-model & version level (dealership sourced). National/ city level

breakdown.

• Rebate: Manufacturer promotion data, includes a breakdown of all promotional activity by OEM’s (dealership sourced).

• Showroom Indicators: (1) Inventory Indicator, (2) Order Indicator, (3) Customer Intention Indicator.

• Volume: CPCA volume data, adjusted using dealership data to provide sales volume mix at a version level. (Imported Models NOT Included).

Legal • They are authorized to sell the data set.

• The data set do not include any PII.

Compliance• The data set does not contain any MNPI.

• The data set is available to any buy side firm

i.e. no exclusivity offered to interested parties.

Related Tickers • Examples include: F:US, GM:US, TM:US, NSANY:US, DAI:GR, VOW:GR, BMW:GR, 2333:HK.

Alpha: Use Cases

• Predict revenue for domestic Chinese manufacturers and revenues generated by foreign manufacturers in mainland China.

• Track discounting and promotional activity of OEM’s on a monthly basis.

• Track inventory levels and average transaction price by brand (sourced from dealership panel).

Alpha: Example

• Eagle Alpha’s first-order autoregressive model for predicting Great Wall Motors revenue, incorporating provider transaction price and volume

data, demonstrates a reduction of mean absolute percentage error of 5.37ppts from (10.28% to just 4.91%), over a baseline model. Directional

accuracy is also markedly improved, increasing from 57.14% to 85.71%.

Trial & SubscriptionE.A Standard Trial Agreement

Yes

Trial Data

Restricted API Access

Trial Duration

Up to 6 weeks

Pricing: Full API Access

$120,000 p.a. per team

This Is An Example Of A Consumer Transaction Dataset

24

Vendor Overview

• This provider is the professional services division one of the world’s largest payment networks by number of cards issued.

• Operating in the Chinese market, they provides end-to-end services of big data analytics and strategy consulting services.

• Their products and services leverage the intelligence derived from the analysis of over 20 billion transactions per year.

Data Set

Description

• Disclaimer by vendor regarding data set: Information provided

through this document is not advice and is subject to change.

We may amend, update, suspend or delete any information in

the content without notice at any time and at our sole

discretion. The provider has represented to Eagle Alpha that

the following information is true at the time of writing [1 Jan

2017]

Data Set Overview

Geography

China

Coverage

800 million card

holders

Mapped to

Tickers

Not yet

History

Since 2011

Collection

Frequency

Monthly, Weekly

Delivery

Frequency

Monthly, Weekly

Lag Time

Monthly: 8 Days

Weekly: 4 Days

Delivery

Method

CSV

Legal & Compliance

• National Monthly/ Weekly Indices: Real Estate, Restaurant & Catering, Luxury Hotel, Economic Hotel, Luxury Automobile, Economic Automobile,

Department Store, Jewellery, Apparel, Luxury, Gas Station, Entertainment, Movie Theatre, Home Appliance, Overseas Spending, E-Commerce.

• Overseas Spending: Monthly/ Weekly Indices in Retail, Restaurant & Catering, Hotel, Duty Free Shops, ATM and more, down to Sector-level.

• Ticker Level Indices (Not yet available): Eagle Alpha have begun testing and development of a suite of ticker level indices with this provider.

Legal

• Eagle Alpha are authorized to sell the data

set.

• The data sets do not include any PII.

Compliance• The data set is available to any buy side firm

i.e. no exclusivity offered to interested parties.

Related Tickers • Examples include: EPA:RMS, EPA:MC, LON:BRBY, EPA:KER, BIT:BMW, ETR:VOW3, ETR:NSU, ETR:DAI

Alpha: Use Cases

• Monthly/Weekly Indices: Closely tracks official data published by the Chinese NBS for floor space of residential buildings sold.

• Overseas Spending: Track spending by China bank card holders abroad at a sector and sub-sector level e.g. luxury retail in Japan or spend in

Macau Casinos.

• Business Intelligence: Predict revenue of companies based on consumer spending in the Chinese market.

Trial & Subscription

E.A Standard Trial

Agreement

No

Trial Data

Historical

Indices

Trial Duration

Up to 6 weeks

National Monthly

Indices: Per Index

$10,000 p.a. per

team

Overseas Spending:

Per Index

$25,000 p.a. per

team

Alpha: Example

• Correlation between the provider’s data for a U.S. tech hardware company’s transactions in China and its sales was +96% from 2014 through

2016, with R-squared of 92%. Eagle Alpha’s autoregressive model built using the vendor’s data demonstrated an MAPE of 13%, and the model

captured major inflection points in revenues over time.

Ticker Level

Indices

Not yet

National Weekly

Indices: Per Index

$15,000 p.a. per

team

Delhi

Mumbai

Bangalore

Business Insights 8

Consumer Credit 4

Consumer Transactions 2

Data Aggregators 5

Employment 1

Geo-Location 2

IoT 1

Open data 4

Pricing 12

Reviews & Ratings 1

Trade 1

Social Media 3

Store Location 1

Web Crawled Data 6

The Alt Data Landscape In India Based On Our Current Database. Last June We Hired A Person Who Speaks Hindi

25

4: Case Studies

• Based on geo-location data from 50 million mobile phones.

• R-Square from a regression with quarterly revenue growth of 50 US retailers was 0.39.

• Average excess return for stocks in the highest quintile was 2.14%, lowest quintile was -1.26%.

Revenue Growth vs. Consumer ActivitiesMarch 2009 – July 2014

Source: National Bureau of Economic Research, June 2016 ,Froot, Kang, Ozik, Sadka

Predictive Value Of Geo-location Data

16

26

• Based on search data from Google Trends.

• Generate search terms, extract search volumes, process the data, test predictive power of each

term.

• Construct the index.

• Measure improvement over a baseline autoregressive index.

Search Data: Key Backtesting Results (Dec 16)

Source: Google Trends, Eagle Alpha

Indicator Comparison Dataset Correlation Improvement over baseline model

Consumer Confidence (UK) Gfk Consumer Confidence 0.96 8%

Mortgage Applications (UK) Mortgage Approvals 0.86 8%

Unemployment (UK) Unemployment Rate (UK) 0.89 13%

Jobs (UK) Claimant Count Change 0.67 5%

Housing (UK) RICS House Price Index 0.6 5%

Unemployment (US) Unemployment Rate (US) 0.9 14%

Jobs (US) Nonfarm Payrolls 0.68 4%

Retail Sales (US) Retail Sales Ex. Autos (US) 0.31 8%

Predictive Value Of Search Data

17

27

• Based on Data collected from listings of jobs indexed exclusively from corporate websites.

• Formed monthly and annual portfolios by dividing the firms into deciles and quintiles based on

postings activity.

• Calculated the hedge returns, i.e. top portfolio return minus bottom portfolio.

• “Jobs Active” metric produced the highest and most consistent returns of 7-9%.

Predictive Value Of Employment Data

18

28

Source: Email Receipt Data, Investment Bank, Eagle Alpha

• Analysis Based on Email Receipt Data for 31 S&P 500 Companies.

• Data was aggregated into a weekly score and week-over-week percentage change was calculated.

• A cross-sectional comparison was performed, long the top 5 stocks and short the bottom 5 stocks.

Annualised Returns 16%Sharpe Ratio 1.13

Email Receipt Data: Cumulative returns using the 4-week z-score

Predictive Value Of Consumer Transaction Data

19

29

• This dataset shows a 99% correlation with

reported revenue for Great Wall Motors and a

95% correlation with YoY revenue growth over

a 5-year period.

• Eagle Alpha’s model for predicting Great Wall

Motors revenue, incorporating this dataset,

demonstrates a mean absolute percentage

error (MAPE) of just 4.9% (Figure 1). The error

rate for market consensus estimates was

8.1% over the same period.

• The information edge over the street estimates

is potentially large. For example, as shown in

Figure 2, in Q4 2014 the CAI data was

forecasting a QoQ growth rate of 48%

compared to the consensus estimate of 17%

versus the reported QoQ growth rate of 42%.

Figure 1: Great Wall Motors Revenue Prediction

Figure 2: Great Wall Motors Revenue Growth vs CAI Dealership Data

Using A Chinese Autos Dataset To Predict Revenue For Great Wall Motors

30

5: About Eagle Alpha

Eagle Alpha’s Vision To Be The ‘Go-To’ Firm For The Alternative Data Needs Of Asset Managers

31

Eagle Alpha’s Solution Is Focused On Education And Alpha

Tailored Teach-in

Firms that want to catch up with the early adopters engage us for an up to 8 hour customized teach-in.

We are the only provider of comprehensive teach-ins regarding alternative data.

Thought Leadership

Firms that want to stay ahead of competitors license this package. It includes events, lessons learnt, proprietary papers and industry developments.

We are the only provider of a dedicated Thought Leadership offering regarding alternative data.

Bespoke Projects

Clients that: a) want to start small; or b) don’t have the skillset; or c) don’t have the capacity, engage Eagle Alpha to do bespoke projects.

Given the other 5 parts of the Eagle Alpha solution we are uniquely positioned to deliver bespoke projects for buyside firms.

Data Insights

Data Insight reports and indicators give clients actionable ideas and demonstrate, how different types of datasets can be leveraged.

We are one of 2 independent firms worldwide that publish “quantamental” research reports that are based on alternative data.

Analytical Tools

We have two tools (Web Queries, Digital Expert Network) that enable clients to conduct proprietary research.

Clients value leveraging our 5 years of experience in order to help tailor the tools to give insight into their research questions.

Data Sourcing

This part of the solution includes: a) a database, b) an advisory service; c) vendor access; and d) proprietary datasets.

We have an unrivalled solution, based on 5 years of experience, to navigate clients through the sea of data.

Education / Best Practice Alpha

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rvie

wU

SP

32

We Are Hosting The BIGGEST Data Showcase Event On 5th December In NYC

33

We Have Already Become A Recognised Leader In The Alternative Data Space e.g. Citi’s Primer Had A 10 Page Profile

34

JPMorgan’s Primer Positioned Eagle Alpha As An Emerging Data Aggregator

35

BoA’s Primer Included Our Taxonomy Of Categories And Positioned Us As A Leading Aggregator

36